LASSO Analysis
library("FRESA.CAD")
library(readxl)
library(igraph)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
opo <- par(no.readonly = TRUE)
load("~/GitHub/BSWiMS/TADPOLE_BSWIMS_Results.RData")
op <- opo
Here we will diagnose ADAS13
LASSOml <- LASSO_1SE(ADAS13~.,TADPOLECrossMRITrain)
pander::pander(t(LASSOml$selectedfeatures))
| Hippocampus | M_ST24TA | M_ST52TA | M_ST13TS | M_ST36TS | M_ST47TS | M_ST56TS | M_ST13SA | M_ST55SA | M_ST60SA | M_ST24CV | M_ST31CV | M_ST32CV | M_ST40CV | M_ST12SV | M_ST17SV | M_ST30SV | RD_ST24TA | RD_ST31TA | RD_ST32TA | RD_ST35TA | RD_ST40TA | RD_ST52TA | RD_ST56TA | RD_ST57TA | RD_ST47TS | RD_ST57SA | RD_ST40CV | RD_ST52CV |
prreg <- predictionStats_regression(cbind(TADPOLECrossMRITest$ADAS13,predict(LASSOml,TADPOLECrossMRITest)),"ADAS13")
ADAS13
pander::pander(prreg)
corci:
| cor | ||
|---|---|---|
| 0.691 | 0.651 | 0.728 |
biasci: -0.292, -0.821 and 0.238
RMSEci: 7.16, 6.81 and 7.56
spearmanci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.667 | 0.622 | 0.707 |
MAEci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 5.57 | 5.25 | 5.91 |
pearson:
| Test statistic | df | P value | Alternative hypothesis | cor |
|---|---|---|---|---|
| 25.4 | 702 | 3.56e-101 * * * | two.sided | 0.691 |
par(op)
LASSODXml <- LASSO_1SE(DX~.,TADPOLE_DX_TRAIN,family="binomial")
prBin <- predictionStats_binary(cbind(TADPOLE_DX_TEST$DX,predict(LASSODXml,TADPOLE_DX_TEST)),"MCI vs Dementia")
MCI vs Dementia
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.786 | 0.736 | 0.836 |
pander::pander(prBin$accc)
| est | lower | upper |
|---|---|---|
| 0.762 | 0.717 | 0.804 |
pander::pander(prBin$berror)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.41 | 0.366 | 0.453 |
pander::pander(prBin$sensitivity)
| est | lower | upper |
|---|---|---|
| 0.235 | 0.155 | 0.331 |
par(op)
LASSODXmlNLDE <- LASSO_1SE(DX~.,TADPOLE_DX_NLDE_TRAIN,family="binomial")
prBin <- predictionStats_binary(cbind(TADPOLE_DX_NLDE_TEST$DX,predict(LASSODXmlNLDE,TADPOLE_DX_NLDE_TEST)),"NL vs Dementia")
NL vs Dementia
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.939 | 0.908 | 0.97 |
pander::pander(prBin$accc)
| est | lower | upper |
|---|---|---|
| 0.86 | 0.814 | 0.899 |
pander::pander(prBin$berror)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.172 | 0.123 | 0.222 |
pander::pander(prBin$sensitivity)
| est | lower | upper |
|---|---|---|
| 0.714 | 0.614 | 0.801 |
par(op)
bConvml <- LASSO_1SE(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
ptestl <- predict(bConvml,TADPOLE_Conv_TEST,type="lp")
boxplot(ptestl~TADPOLE_Conv_TEST$status)
ptestr <- exp(ptestl)
boxplot(ptestr~TADPOLE_Conv_TEST$status)
perdsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
if (max(ptestl)>0 && min(ptestl)<0 )
{
prSurv <- predictionStats_survival(perdsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
pander::pander(prSurv$CILp)
pander::pander(prSurv$spearmanCI)
}
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to AD Conversion")
MCI to AD Conversion
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.834 | 0.784 | 0.885 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 0 | 0 | 0 |
| Test - | 98 | 175 | 273 |
| Total | 98 | 175 | 273 |
par(op)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.372 | 0.187 | 0.54 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TESTD$status,ptestl),"MCI to AD Conversion")
MCI to AD Conversion
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.836 | 0.787 | 0.885 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 16 | 4 | 20 |
| Test - | 82 | 171 | 253 |
| Total | 98 | 175 | 273 |
par(op)
.